Simulation and Optimization Methods in Machining and Structure/Material Design

1. Introduction and Scope

Machining and structural/material design are fundamental pillars of modern manufacturing systems. As industries continue to demand higher performance, higher efficiency, and tighter tolerances, the design and control of manufacturing processes have become more intricate than ever. These involve a wide array of complex physical interactions that are inherently nonlinear and time-dependent. Factors such as process parameters, thermo-mechanical coupling [1], phase transformation, and microstructural evolution [2,3] are deeply interconnected, collectively influencing product performance, manufacturing efficiency, and cost. The simultaneous occurrence of heat transfer, stress development, material flow, solidification, and microstructural change presents significant challenges for both real-time process control and predictive design [4].

Understanding and mastering these phenomena require more than empirical experience—it demands a scientific, physics-based modeling approach. To this end, numerical modeling and multi-scale simulation techniques have emerged as indispensable tools in both academic and industrial domains [5,6]. These tools enable engineers and scientists to visualize and quantify internal states and transitions that are otherwise difficult to observe directly, such as grain boundary movement, dislocation activity, residual stress evolution, and thermal field distribution [7,8]. By capturing behaviors across multiple length and time scales, from atomic arrangements to component-level responses, multi-scale modeling provides a comprehensive framework for linking processing conditions to material outcomes [9,10]. In recent years, the integration of simulation technologies with advanced optimization algorithms, such as machine learning, evolutionary computing, and surrogate modeling, has further enhanced the ability to explore large design spaces efficiently [11,12]. These intelligent methods, when combined with systematic experimental validation, help to minimize dependence on costly and time-consuming trial-and-error experimentation. Moreover, the adoption of digital twins, physics-informed neural networks (PINNs), and real-time data assimilation into simulation environments is further narrowing the gap between modeling and practical application [13,14,15]. These developments are accelerating the convergence of virtual prototyping and smart manufacturing. As a result, product development cycles can be shortened, manufacturing processes can be fine-tuned in silico, and overall production costs can be significantly reduced without compromising performance. These advances also open new opportunities for material-by-design strategies, where optimal microstructures and component geometries can be predicted before fabrication, significantly reducing waste and resource consumption.

This Special Issue presents a curated collection of recent advances in the field of machining and structural/material design, with particular emphasis on the simulation and optimization of complex processing phenomena. The selected papers represent a broad and balanced mix of fundamental theory, computational methodology, and application-oriented research. Together, they showcase how simulation-driven innovation continues to reshape the understanding and advancement of modern manufacturing systems. One of the key thematic focuses of this collection is multi-physics modeling, a critical approach for capturing the coupled behavior of different physical fields in real processing environments. The papers delve into various modeling strategies, including coupled thermal–mechanical simulations that enable more accurate predictions of temperature fields, stress distribution, and deformation during manufacturing processes such as welding, forming, and additive manufacturing. Several contributions further investigate the quantitative relationship between microstructure evolution and mechanical properties, providing insight into the process–structure–property triad and enabling performance-driven material design. Another important dimension addressed by this Special Issue is real-time process modeling and feedback control. Some papers explore how in situ monitoring data can be integrated into simulation frameworks to support adaptive control, defect prevention, and quality assurance in advanced manufacturing settings. This reflects a growing trend toward digital twin technologies, which fuse real-time sensor data with predictive models to optimize performance dynamically. In addition, the integration of simulation tools with material characterization techniques and optimization algorithms is a recurring highlight throughout the collection. The use of artificial intelligence, surrogate modeling, and metaheuristic algorithms to guide process design and parameter tuning is gaining momentum, especially in high-dimensional or computationally expensive scenarios. These approaches not only enhance predictive accuracy but also significantly reduce development costs and time. Collectively, this Special Issue reflects a strong and growing commitment within the research community to build robust, accurate, and scalable simulation and optimization frameworks. These frameworks are essential for enabling the next generation of intelligent, data-driven, and sustainable manufacturing solutions, supporting the transformation toward smart factories, autonomous decision-making, and agile production paradigms.

2. Contributions

In this Special Issue, twenty high-quality papers contributed by distinguished researchers from diverse academic and industrial backgrounds have been published, reflecting the current forefront of research and innovation in the areas of machining and structure/material design. These contributions collectively highlight the growing maturity and versatility of simulation and optimization methods as powerful tools for addressing complex, multidisciplinary challenges inherent to modern manufacturing systems. As manufacturing transitions toward higher performance, digitalization, and sustainability, the need for predictive modeling, data-driven decision-making, and physics-informed optimization has become more pronounced than ever. The scope of the published works ranges from fundamental investigations into material behavior and process–structure–property relationships to application-driven innovations in process control, energy efficiency, and structural performance enhancement. Several studies explore advanced numerical techniques such as finite element modeling, phase-field simulation, and molecular dynamics to capture multi-scale and multi-physics interactions in real manufacturing environments. Others integrate intelligent algorithms—ranging from machine learning to evolutionary optimization—with experimental validation to accelerate process development and enhance design reliability. The material systems covered include conventional steels, high-strength alloys, titanium-based materials, and complex composites, with applications that extend across automotive, aerospace, energy, and biomedical sectors. Collectively, these twenty papers demonstrate how the synergy between simulation, optimization, and experimentation is driving a new paradigm in materials and process engineering. By enabling more accurate predictions, rapid iteration, and integrated evaluation, these approaches contribute significantly to reducing development time, improving product performance, and ensuring more robust and adaptive manufacturing practices. This Special Issue not only showcases recent technical progress but also outlines a promising path forward for the continued evolution of intelligent, efficient, and sustainable design and production methodologies in advanced manufacturing.

Among the contributions, five papers concentrated on material forming processes and the quantitative relationships between processing conditions, microstructure evolution, and resulting mechanical properties. These studies represent comprehensive efforts to understand and control material response under complex thermal–mechanical environments, where the accurate modeling of heat flow, plastic deformation, and microstructural transitions is crucial for ensuring component integrity and performance. Specifically, one paper investigated how varying tool rotation speeds affected the degree of dynamic recrystallization, grain size distribution, and mechanical strength in a high-copper-content aluminum alloy subjected to friction stir welding, offering insights into the optimization of welding parameters for strength–ductility balance (contribution 1). Another work constructed forming limit diagrams (FLDs) for 22MnB5 hot-stamped steel through a combination of experiments and finite element simulations, thereby providing practical references for predicting fracture onset and improving die design in automotive hot forming (contribution 2). A third study focused on the deformation mechanisms of 42CrMo high-strength steel during multi-pass warm upsetting, revealing the influence of temperature, strain rate, and pass schedule on strain accumulation and property uniformity, which is critical for large-diameter shaft or gear component manufacturing (contribution 3). The fourth paper evaluated how laser power, scanning speed, and hatch spacing influenced porosity, melt pool stability, and microstructure in TA15 titanium alloy fabricated via selective laser melting (SLM), and correlated these parameters with tensile strength and elongation, supporting quality control in additive manufacturing of high-performance Ti alloys (contribution 4). Lastly, the microstructural evolution and texture changes in an Al-Mg-Si-Cu alloy under different welding speeds in friction stir welding were examined, demonstrating how controlled welding conditions could refine equiaxed grains, reduce anisotropy, and improve performance stability across the weld zone (contribution 5).

In addition, two papers focused on cutting and machining behavior modeling, which are critical for understanding thermal load effects, tool wear mechanisms, and energy efficiency in precision machining. The first study developed an analytical–numerical hybrid model for predicting transient temperature profiles in cutting tools under varying turning parameters, considering heat generation, conduction, and convection at the tool–chip–workpiece interface, and validated the model with embedded thermocouple measurements (contribution 6). The second study explored how drilling energy consumption relates to changes in subsurface microhardness, residual strain, and plastic deformation in the material beneath the machined surface, highlighting how energy efficiency metrics can serve as indirect indicators of machining-induced damage and surface integrity degradation (contribution 7).

Three additional papers emphasized microstructure-level simulations and the interplay of multiple physical fields during advanced material processing. These contributions reflect the increasing use of high-fidelity computational models to simulate fine-scale interactions that are difficult to observe experimentally. One study employed a phase-field method to simulate dendritic growth in a WC particle-reinforced Ni-based alloy during plasma cladding, incorporating residual stress fields into the model and demonstrating how mechanical loading alters solute diffusion and interface morphology, thus guiding parameter control for composite coatings (contribution 8). Another used molecular dynamics (MD) to investigate the structure and thermal response of Ni₆₀Nb₄₀ metallic glass in particle and thin film forms, revealing that reduced dimensions lower the glass transition temperature and affect atomic packing efficiency, which has implications for nanoscale amorphous material design and thermal stability assessment (contribution 9). The final paper in this group developed a finite element model that links plastic deformation in 35CrMoA steel to ultrasonic nonlinear responses, specifically second harmonic generation. By coupling dislocation density evolution with acoustic wave propagation modeling, this work advanced our ability to noninvasively detect early-stage plastic damage, contributing to nondestructive evaluation (NDE) techniques in safety-critical components (contribution 10).

One notable paper in this collection delivered an innovative contribution to the field of architected materials and structural mechanics. The authors proposed and experimentally verified a family of metallic re-entrant auxetic structures with negative Poisson’s ratio behavior, focusing on the influence of geometric parameters on mechanical responses such as energy absorption, elastic recovery, and cyclic stability. This work provides valuable insights into the design of lightweight, flexible metamaterials that are applicable to fields ranging from aerospace to biomedical devices, where both strength and adaptability are required (contribution 11).

Five additional papers focused on optimization strategies in processing and structural/material design. These contributions demonstrate the growing integration of intelligent algorithms and computational tools to enhance material performance, structural efficiency, and manufacturing sustainability. One study optimized the microstructure and mechanical properties of alloys through multiple remelting and heat treatment cycles, improving the overall performance and reliability of the materials (contribution 12). Another employed an Extreme Learning Machine (ELM) surrogate model combined with a Particle Swarm Optimization (PSO) algorithm to optimize the dynamics of multiblade centrifugal fans, enhancing aerodynamic performance and operational stability (contribution 13). A third work carried out a multi-objective lightweight design of vehicle aluminum subframes, achieving a balance between strength, stiffness, and weight, which supports the development of fuel-efficient vehicles (contribution 14). The fourth paper optimized hopper structure using an E-SVR surrogate model to improve material flow behavior and device efficiency, contributing to smarter material handling system design (contribution 15). The final study in this group focused on improving the energy efficiency of large-scale automotive glass molding processes, successfully reducing energy consumption and carbon emissions, and advancing green manufacturing practices (contribution 16).

Finally, four comprehensive review papers examined simulation and optimization methods in machining and structural/material design, covering several cutting-edge technologies and emerging application directions. These contributions highlight the growing role of intelligent computation and advanced modeling in enhancing manufacturing precision, efficiency, and innovation. One review focused on the application of artificial intelligence in the design and analysis of steel structures, emphasizing how intelligent algorithms significantly improve design efficiency and accuracy, while enabling optimization under increasingly complex conditions (contribution 17). Another provided an overview of recent advances in the finite element modeling of electrical discharge machining (EDM), analyzing how this method effectively simulates material removal and temperature field distribution, thereby offering a theoretical basis for process parameter optimization (contribution 18). A third paper surveyed the use of swarm intelligence algorithms to optimize EDM parameters, with notable achievements in enhancing machining accuracy, reducing surface damage, and improving overall efficiency—demonstrating the strong potential of swarm intelligence in advanced manufacturing (contribution 19). The final review explored ultrasound-assisted wire-arc additive manufacturing, outlining its key role in improving forming precision, shortening production cycles, and reducing costs, which supports innovation in metal rapid prototyping technologies (contribution 20). Collectively, these studies offer innovative optimization strategies and engineering insights that support material processing, mechanical design, and industrial technology upgrading.

As Guest Editors of this Special Issue, we hope that the papers included in this collection will be beneficial to scientists and engineers in advancing their research and development endeavors.

3. Conclusions and Outlook

In the fields of machining and structure/material design, simulation and optimization methods have been a hot topic in academic research and industrial applications, and have been evolving with the progress of science and technology. With the continuous integration of multidisciplinary technologies, especially the combination of computer science [16], materials science, and engineering technology [17], numerical models and algorithms for simulating machining processes and structural responses have been making significant progress. Currently, the conditions for accurate simulation are continuously improving, enabling the more accurate reflection of various physical phenomena in complex machining environments, such as stress distribution, thermal field changes, and the evolution of material properties [18].

In terms of optimization decisions, technological developments have greatly expanded the range of factors that can be taken into account. In the past, optimization methods usually focused on the adjustment of a single process parameter, but currently, with the proposal of multi-objective optimization, researchers have begun to focus on a more complex decision space, such as the collaborative optimization of multiple factors, including material properties, production costs, environmental impacts, process stability, and so on. This not only improves the overall performance of products but also promotes the exploration of more sustainable manufacturing models [19]. In particular, with the combination of advanced physical models, intelligent optimization algorithms, and large-scale material property databases, researchers are able to accurately simulate the deformation and phase transition processes of multiphase materials under different processing conditions, as well as their interactions with environmental factors. For example, with applications in industrial processes such as machining, mold making, and composite molding, these simulation tools provide more reliable theoretical support for the production process, helping to reduce unnecessary test costs, improve productivity, and reduce energy consumption [20].

However, despite the advances in new numerical techniques, machine learning methods, and computational power, the fields of machining and structure/material design still face many challenges in modeling and optimization. For complex multiphase materials, it is still a considerable challenge to accurately simulate their behavioral changes under different machining environments. Moreover, with the continuous upgrading of industrial demands, existing optimization algorithms often display inefficiency and lack of accuracy when dealing with large-scale, multi-objective complex systems. Therefore, how to design efficient optimization algorithms to adapt to the changing industrial demands is an important trend for future research.